Traditional search engines match keywords. If you search for "dog shelters around Gurgaon" and the indexed page says "animal shelters near Delhi," you get no results. The words do not overlap. Semantic search fixes this by converting text into vectors. Similar ideas end up close together in vector space, even when the words differ. An embedding model takes a word or sentence and produces a high-di
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode
Want to keep Ubuntu updated? Run these commands on your PC via terminal, or create a .sh file and add the following commands: Bash If you don't know how to create the .sh file, no problem! Just navigate to the folder in your terminal and run the following: touch file.sh ----> creates the file nano file.sh -----> opens the file in the terminal Finally, add the previous commands to the file, press C